Computing and Information Systems - Theses
Permanent URI for this collection
Search Results
1 - 1 of 1
-
ItemEnergy and Time Aware Scheduling of Applications in Edge and Fog Computing Environments( 2022)The Internet of Things (IoT) paradigm is playing a principal role in the advancement of many application scenarios such as healthcare, smart city, transportation, entertainment, and agriculture, which significantly affect the daily life of humans. The smooth execution of these applications requires sufficient computing and storing resources to support the massive amount of data generated by IoT devices. However, IoT devices are resource-limited intrinsically and are not capable of efficient processing and storage of large volumes of data. Hence, IoT devices require surrogate available resources for the smooth execution of their heterogeneous applications, which can be either computation-intensive or latency-sensitive. Cloud datacenters are among the potential resource providers for IoT devices. However, as they reside at a multi-hop distance from IoT devices, they cannot efficiently execute IoT applications, especially latency-sensitive ones. Fog computing paradigm, which extends Cloud services to the edge of the network within the proximity of IoT devices, offers low latency execution of IoT applications. Hence, it can improve the response time of IoT applications, service startup time, and network congestion. Also, it can reduce the energy consumption of IoT devices by minimizing their active time. However, Fog servers are resource-limited compared to Cloud servers, preventing them from the execution of all types of IoT applications, especially extremely computation-intensive applications. Hence, Cloud servers are used to support Fog servers to create a robust computing environment with heterogeneous types of resources. Consequently, the Fog computing paradigm is highly dynamic, distributed, and heterogeneous. Thus, without efficient scheduling techniques for the management of IoT applications, it is difficult to harness the full potential of this computing paradigm for different IoT-driven application scenarios. This thesis focuses on different scheduling techniques for the management of IoT applications in Fog computing environments while considering: a) IoT devices' characteristics, b) the structure of IoT applications, c) the context of resource providers, d) the networking characteristics of the Fog servers, e) the execution cost of running IoT applications, and f) the dynamics of computing environment. This thesis advances the state-of-the-art by making the following contributions: 1. A comprehensive taxonomy and literature review on the scheduling of IoT applications from different perspectives, namely application structure, environmental architecture, optimization properties, decision engine characteristics, and performance evaluation, in Fog computing environments. 2. A distributed Fog-driven scheduling technique for network resource allocation in dense and ultra-dense Fog computing environments to optimize throughput and satisfy users' heterogeneous demands. 3. A distributed scheduling technique for the batch placement of concurrent IoT applications to optimize the execution time of IoT applications and energy consumption of IoT devices. 4. A distributed application placement and migration management technique to optimize the execution time of IoT applications, the energy consumption of IoT devices, and the migration downtime in hierarchical Fog computing environments. 5. A Distributed Deep Reinforcement Learning (DDRL) technique for scheduling IoT applications in highly dynamic Fog computing environments to optimize the execution time of IoT applications and energy consumption of IoT devices. 6. A system software for scheduling IoT applications in multi-Cloud Fog computing environments. 7. A detailed study outlining challenges and new research directions for the scheduling of IoT applications in Fog computing environments.